Turn a discrete-event game run into a (group, card) behaviour frame

Reshapes the event log of a sdt_simulate() run into the same one-row-per (group, card) frame that sdt_fidelity() scores, so the discrete-event engine can be held to exactly the same statistics as a fitted behaviour model. Each replication is one group and each casualty id is one card, which is what makes the comparison legitimate: groups within a real course session play the same fixed deck, and so do replications.

Usage

sdt_run_behaviour(run, group = "group_id", card = "card")

Arguments

run: A game_run from sdt_simulate().
group: Name for the group column in the result.
card: Name for the card column in the result.

Value

A tibble with one row per (replication, casualty), the two key columns named by group and card, the cohort's triage and therapy, the casualty's end_area, and one logical column per behaviour described above.

What each column means, and what it cannot mean

red, yellow, green, ETC, DCS, TASC and ventilated are read off the cohort. The engine routes on them, it does not choose them, so they are identical in every replication and their between-group SD is exactly zero. That is not a bug in this function; it is the engine's structure made visible, and sdt_fidelity() will report it as an infinite dispersion ratio.
surgery, diagnostics, icu, ward, recovery, green_area record whether the casualty ever entered that board area. These can vary between replications, through queueing and the horizon.
died is end_area == "morgue", and not_seen is a casualty the game ended before. The engine has no branch to the morgue, so died is always FALSE.

The observed counterparts, for a course export, are therapy == "TASC" and so on for the therapy concepts, called_triage == "red" for the sighting categories, therapy != "none" for surgery, and the reported end location for icu. There is deliberately no eFAST or CT column: the engine has a single radiology area and no modality, so inventing one would be inventing a comparison.

Examples

co <- sdt_synthetic_cohort(n = 12, seed = 1)
run <- sdt_simulate(sdt_scenario(co), replications = 3, seed = 1,
                    verbose = FALSE)
sdt_run_behaviour(run)
#> # A tibble: 36 × 20
#>    group_id card  triage therapy ventilated red   yellow green ETC   DCS   TASC 
#>    <chr>    <chr> <chr>  <chr>   <lgl>      <lgl> <lgl>  <lgl> <lgl> <lgl> <lgl>
#>  1 rep001   P001  red    TASC    TRUE       TRUE  FALSE  FALSE FALSE FALSE TRUE 
#>  2 rep001   P002  red    TASC    TRUE       TRUE  FALSE  FALSE FALSE FALSE TRUE 
#>  3 rep001   P003  green  none    FALSE      FALSE FALSE  TRUE  FALSE FALSE FALSE
#>  4 rep001   P004  yellow TASC    FALSE      FALSE TRUE   FALSE FALSE FALSE TRUE 
#>  5 rep001   P005  red    DCS     FALSE      TRUE  FALSE  FALSE FALSE TRUE  FALSE
#>  6 rep001   P006  yellow TASC    FALSE      FALSE TRUE   FALSE FALSE FALSE TRUE 
#>  7 rep001   P007  yellow TASC    FALSE      FALSE TRUE   FALSE FALSE FALSE TRUE 
#>  8 rep001   P008  green  none    FALSE      FALSE FALSE  TRUE  FALSE FALSE FALSE
#>  9 rep001   P009  green  none    FALSE      FALSE FALSE  TRUE  FALSE FALSE FALSE
#> 10 rep001   P010  red    TASC    FALSE      TRUE  FALSE  FALSE FALSE FALSE TRUE 
#> # ℹ 26 more rows
#> # ℹ 9 more variables: surgery <lgl>, diagnostics <lgl>, icu <lgl>, ward <lgl>,
#> #   recovery <lgl>, green_area <lgl>, end_area <chr>, died <lgl>,
#> #   not_seen <lgl>